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Purpose
To reduce noise and artifacts in 23Na MRI with a Compressed Sensing reconstruction and a learned dictionary as sparsifying transform.
Methods
A three‐dimensional dictionary‐learning compressed sensing reconstruction algorithm (3D‐DLCS) for the reconstruction of undersampled 3D radial 23Na data is presented. The dictionary used as the sparsifying transform is learned with a K‐singular‐value‐decomposition...
Sodium (23Na) MRI is a noninvasive tool to assess cell viability, which is linked to the total tissue sodium concentration (TSC). However, due to low in vivo concentrations, 23Na MRI suffers from low signal-to-noise ratio (SNR) and limited spatial resolution. As a result, image quality is compromised by Gibbs ringing artifacts and partial volume effects. An iterative reconstruction algorithm that...
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